Non-hydrogen-bonding universal reader for dna sequencing

ABSTRACT

The present disclosure provides apparatus and methods for determining the sequence of a nucleic acid. The apparatus comprises electrodes that form a tunnel gap through which the nucleic acid can pass. The electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. When the reagent interacts with a nucleobase, a detectable signal is produced and used to identify the nucleobase of the nucleic acid. Advantageously, the apparatus of this disclosure is specific to identifying nucleic acids.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No. 16/327,232, filed on Feb. 21, 2019 (published as US20190195856), which is the U.S. National Stage of International Application No. PCT/US2017/047818, filed on Aug. 21, 2017, which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/378,033, filed on Aug. 22, 2016, the contents of each of which are incorporated herein by reference in their entireties.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under R01 HG006323 awarded by the National Institutes for Health. The government has certain rights in the invention.

BACKGROUND

DNA sequencing, particularly Next Generation Sequencing (NGS),¹ is the most powerful technology in genomic analysis at the present. NGS can sequence an individual human genome in a few days at a cost of $1000. Compared to conventional Sanger sequencing, however, NGS has lower read accuracy and shorter read length.² A recent study has warned that the reproducibility of single nucleotide variants (SNVs) calls was only around 80% even using the highest stringency of QC metrics with SOLiD sequencing, a NGS technology that has sequencing accuracy higher than others.³ NGS also faces another great challenge of determining long repetitive regions in a genome. Although single molecule real time (SMRT) sequencing provides a long-read solution for the issue (see the world wide web (www) at pacb.com), it produces sequences at a much higher error rate per base than NGS does. Currently, DNA sequencing by synthesis is a dominated technology, and its accuracy is limited by the fidelity of polymerases, the error rates of which are on the order of 10⁻⁵ to 10⁻⁷ per base pair from commercially available products.^(4,5) Given that the somatic mutation rate in human B and T lymphocytes and in fibroblasts are on the order of 2 to 10 mutations per diploid genome per cell division,⁶ an ideal DNA sequencer should have an error rate lower than the mutation rate of 10⁻⁹ per base. Moreover, since approximately 50% of the human genome is comprised of repeats with their lengths in a range of 2 to 100,000 bp,⁷ the sequencer should have a read length of >100 kbp, and ideally be able to read a chromosome from one end to another. For use in clinics, it should have a single molecule sensitivity for rare genetic variants, be able to sequence a human genome for less than $100, and simple to operate.⁸

Sequencing by protein nanopores has proven that a DNA sequence can directly be read out based on physical properties of nucleobases. As an example, MinION the commercial version of a protein nanopore sequencer can currently achieve a read-length 98 kb⁹ while the theoretical read length is unlimited. However, the nanopore sequencing has a high error rate per base read 15%). In addition to stochastic motions of the single DNA molecule in the pore, the error rate is due to the overlapped ionic current levels, as five nucleotides contribute to a current blockade in the nanopore,¹⁰ which results in 4⁵ or 1054 possible 5-mers needed to be assigned, let alone the existence of modified bases in the genome. Although improved data analysis has increased the accuracy of MinION significantly,¹¹ a technology breakthrough is essential to improve the spatial resolution of nanopores to a single nucleotide so that the assignment will be reduced to distinguishing among the four naturally occurring nucleobases plus their various modified forms. Thinner nanopores have been studied to improve the resolution of DNA sequencing. For example, it has been demonstrated that a Mycobacterium smegmatis porin A (MspA) pore reads DNA by a block of four nucleotides (quadromer) at a time, better than the α-hemolysin pore of the MinION.¹² The MspA nanopore has a funnel shape with a constriction region of 1.2 nm in diameter and 0.6 nm in length,¹³ smaller than α-hemolysin that has a constriction site of 1.4 nm in diameter, followed by a β-barrel of about 5 nm long and 2 nm wide. Since an ionic blockade is sensitive to the DNA bases lying in the outsides of a nanopore as well,¹⁴ it is unlikely for the ionic measurement to achieve a single nucleotide resolution ever in an atomically thin nanopore (such as a graphene nanopore).

Electron tunneling in a nanogap offers an alternative readout to improve the accuracy of nanopore sequencing. Zwolak and DiVentra first proposed to sequence DNA using changes in tunnel current flowing transverse to the DNA axis.¹⁵ Taniguchi and Kawai et al. have demonstrated that nucleoside and deoxynucleoside monophosphates can generate characteristic tunneling currents through bare gold gaps of 0.8 to 1.0 nanometer wide.^(16,17) This is too small for a gap size to pass an intact single stranded DNA. A second problem with bare gold electrodes lies in strong chemisorption of adenine^(18, 19, 20) and rapid contamination on their surfaces. U.S. Pat. No. 8,628,649 discloses a Recognition Tunneling (RT) technique to read nucleobases using recognition molecules covalently attached to two electrodes that interspace a nanogap (˜2.5 nm wide). The recognition molecule is able to effectively interact with each individual nucleobase to generate distinguishable electrical signals, so-called a universal reader. Since each nucleobase contains multiple hydrogen bonding sites, Liang et al.'s universal reader, 4(5)-(2-mercaptoethyl)-1H-imidazole-2-carboxamide (Iz) was designed to bear multiple hydrogen bond donors and acceptors.²¹ Iz is extremely versatile and its interaction goes beyond nucleobases, such as with amino acids, peptides, and carbohydrates.^(23,24) Thus, there remains a need for a highly selective universal reader that only recognizes nucleobases.

Citation of any reference in this section is not to be construed as an admission that such reference is prior art to the present disclosure.

SUMMARY

The present disclosure provides an apparatus and a method for determining the sequence of a nucleic acid. The apparatus comprises electrodes that form a tunnel gap through which the nucleic acid can pass. The electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. When the reagent interacts with a nucleobase, a detectable signal is produced and used to identify the nucleobase of the nucleic acid. Advantageously, the apparatus of this disclosure is specific to identifying nucleic acids.

The present disclosure also provides compounds that can be used as reagents for attaching to electrodes used in the apparatus of the disclosure. The compound has formula (I):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; R₂ is selected from the group consisting of thiol, disulfide, and amine; and R₃ is H, an electron withdrawing group or an electron donating group; ring A or ring B is substituted with R₁-R₂; and ring C or ring D is substituted with R₃; provided that (i) when ring A is substituted with R₁-R₂, ring C is substituted with R₃; and (ii) when ring B is substituted with R₁-R₂, ring D is substituted with R₃.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A shows hydrogen bonding sites (D: hydrogen bonding donor; A: hydrogen bonding acceptor) and dipole moments (R═CH₃) of DNA bases; FIG. 1B shows the chemical structures of reader molecules Iz (imidazole-2-carboxamide), Py (pyrene), and Bn (benzene); and FIG. 1C shows computer modeling on reader molecules interacting with deoxyguanosine monophosphate (dGMP) in tunneling gaps created by fixing the distance between two sulfur atoms and then carrying out the energy minimization in software Spartan'14.

FIG. 2 shows an energy-minimized structure of a π-π stacked complex between two pyrene rings (attached to gold electrodes) and a guanine.

FIG. 3 shows an energy optimized structure of the π-π stacked complex between two pyrene rings (attached to gold electrodes/metal slabs) and a uracil.

FIG. 4 shows a graph of calculated current vs bias. The top line with round dots is for the complex from FIG. 2 and the bottom line with square dots is the complex from FIG. 3.

FIG. 5 show an XPS spectrum of Py SAM on Palladium.

FIG. 6A and FIG. 6B show the FT-IR spectra of Py powder (FIG. 6A) and Py SAM on palladium (FIG. 6B).

FIG. 7A and FIG. 7B show the FT-IR spectra of Bn neat (FIG. 7A) and Bn SAM on palladium (FIG. 7B).

FIG. 8 shows a surface plasmon resonance sensorgram illustrating the response of a gold chip derivatized according to the present disclosure to dGMP.

FIG. 9A-FIG. 9C show analysis of RT raw data. FIG. 9A shows primary parameters in the time domain. FIG. 9B shows secondary features in frequency domain. FIG. 9C shows secondary features in cepstrum domain.

FIG. 10A-FIG. 10X show plots to display distributions of the same feature from two different DNA nucleotides using a single feature data that can only be assigned to one analyte with a probability only marginally above random, or equal to P=0.5 in the most cases.

FIG. 11A-FIG. 11T show RT spectra generated with: Iz functionalized tip and substrate at a set point of 4 pA and 0.5 V (FIG. 11A-FIG. 11F); Py functionalized tip and substrate at a set point of 2 pA and 0.5 V (FIG. 11G-FIG. 11L); Bn functionalized tip and substrate at a set point of 2 pA and 0.5 V (FIG. 11M-FIG. 11P); Bn functionalized tip and Py substrate at a set point of 2 pA and 0.5 V (FIG. 11Q-FIG. 11T).

FIG. 12A-FIG. 12F show histograms of peak height (FIG. 12A and FIG. 12D), averaged amplitude (FIG. 12B and FIG. 12E), peak width (FIG. 12C and FIG. 12F), and their fitting curves of current spikes from recognition of DNA nucleotides by Py at the set point of 0.5 V and 2 pA; and by Iz at the set point of 0.5 V and 4 pA.

FIG. 13A and FIG. 13B show a plot for nucleotide calling accuracy vs number of used signal features with Py (FIG. 13A) and with Iz (FIG. 13B).

FIG. 14 shows a plot of accuracy determined by SVM analysis of AP (read with Iz) and four DNA nucleotides (read with Py).

DETAILED DESCRIPTION

The invention includes the following:

(1.) An apparatus for analyzing a nucleic acid sequence in a sample, the apparatus comprising a chamber, wherein the chamber comprises:

-   -   (a) a first and a second electrode that form a tunnel gap         through which the nucleic acid sequence can pass;     -   (b) a first reagent attached to the first electrode and a second         reagent attached to the second electrode, wherein the first and         the second reagent are capable of selectively interacting with a         nucleobase of the nucleic acid sequence, wherein a detectable         signal is produced when the nucleobase interacts with the first         and second reagent.         (2.) An apparatus for selectively analyzing a nucleic acid         sequence in a sample, the apparatus comprising a chamber,         wherein the chamber comprises     -   (a) a first and a second electrode that form a tunnel gap         through which the nucleic sequence can pass;     -   (b) a first reagent attached to the first electrode and a second         reagent attached to the second electrode, wherein the first and         the second reagent comprise an aromatic compound, wherein a         detectable signal is produced when the nucleobase of the nucleic         acid sequence interacts with the first and second reagent.         (3.) The apparatus according to the above (1.) or (2.), wherein         the first and/or second electrode comprise a metal selected from         the group consisting of palladium, gold, grapheme, carbon         nanotube, and molybdenum disulphide.         (4.) The apparatus according to the above (1.) or (2.), wherein         the first and/or second electrode comprise palladium.         (5.) The apparatus according to any one of the above (1.) to         (4.), wherein the first and second reagent are the same.         (6.) The apparatus according to any one of the above (1.), (3.)         or (4.), wherein the first and/or second reagent is an aromatic         compound.         (7.) The apparatus according to the above (2.) or (6.), wherein         the aromatic compound is selected from the group consisting of         pyrene, benzene, anthracene, benzo[e]pyrene,         2-(phenylethynyl)pyrene, 2-phenyl pyrene, 3-nitro-1H-pyrrole and         5-nitro-1H-indole, wherein the polycyclic aromatic compound is         unsubstituted or substituted with a substituent selected from         nitro, phenyl, (C₁-C₆)alkyl substituted with phenyl,         (C2-C6)alkenyl substituted with phenyl and (C₂-C₆)alkynyl         substituted with phenyl.         (8.) The apparatus according to the above (7.), wherein the         aromatic compound is selected from the group consisting of         pyrene, 1-(2-mercaptoethyl)pyrene, nitrobenzene,         2-nitroanthracene, benzo[e]pyrene, 2-(phenylethynyl)pyrene,         2-phenyl pyrene, 3-nitro-1H-pyrrole and 5-nitro-1H-indole.         (9.) The apparatus according to the above (7.), wherein the         aromatic compound is a pyrene.         (10.) The apparatus according to the above (9.), wherein the         aromatic compound is a pyrene substituted with         (C₁-C₆)mercaptoalkyl.         (11.) The apparatus according to any one of the above (1.) to         (10.), wherein the aromatic compound is         1-(2-mercaptoethyl)pyrene.         (12.) The apparatus according to any one of the above (1.) to         (4.), wherein the aromatic compound comprises a thiol, disulfide         or amine.         (13.) The apparatus according to the above (12.), wherein the         aromatic compound comprises a thiol.         (14.) The apparatus according to any one of the above (1.) to         (13.), wherein the tunnel gap has a width of about 1.0 nm to         about 5.0 nm.         (15.) The apparatus according to any one of the above (1.) to         (14.), further comprising a detector for measuring the         detectable signal.         (16.) The apparatus according to any one of the above (1.) to         (15.), further comprising a system for introducing and removing         buffer and the sample into the chamber.         (17.) The apparatus according to any one of the above (1.) to         (16.), further comprising a system for analyzing the detectable         signal.         (18.) The apparatus according to any one of the above (1.) to         (17.), wherein the sample is a biological sample.         (19.) The apparatus according to any one of the above (1.) to         (18.), wherein the nucleic acid is DNA.         (20.) The apparatus according to any one of the above (1.) to         (18.), wherein the nucleic acid is RNA.         (21.) The apparatus according to any one of the above (1.) to         (18.), wherein the nucleic acid is PNA         (22.) The apparatus according to any one of the above (1.) to         (18.), wherein the nucleic acid is XNA         (23.) The apparatus according to any one of the above (1.) to         (18.), wherein the nucleic acid comprises unnatural bases.         (24.) A compound of formula (I):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; R₂ is selected from the group consisting of thiol, disulfide, and amine; and R₃ is H, an electron withdrawing group or an electron donating group; ring A or ring B is substituted with R₁-R₂; and ring C or ring D is substituted with R₃; provided that (i) when ring A is substituted with R₁-R₂, ring C is substituted with R₃; and (ii) when ring B is substituted with R₁-R₂, ring D is substituted with R₃. (25.) The compound of the above (24.), wherein R₁ is (C₁-C₆) alkyl. (26.) The compound of the above (24.), wherein R₁ is (C₁-C₃) alkyl. (27.) The compound of the above (24.), wherein R₁ is ethyl. (28.) The compound of any one of the above (24.) through (27.), wherein R₂ is thiol. (29.) The compound of any one of the above (24.) through (28.), wherein R₃ is H, —NO₂ or CH₃. (30.) The compound of any one of the above (24.) through 25, wherein R₃ is H. (31.) The compound of the above (24.), wherein said compound is formula (Ia):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; and R₂ is selected from the group consisting of thiol, disulfide, and amine. (32.) The compound of the above (31.), wherein R₁ is (C₁-C₆) alkyl. (33.) The compound of the above (31.), wherein R₁ is (C₁-C₃) alkyl. (34.) The compound of the above (31.), wherein R₁ is ethyl. (35.) The compound of any one of the above (31.) through (34.), wherein R₂ is thiol. (36.) A method of determining the sequence of a nucleic acid, the method comprising

-   -   (a) providing an apparatus according to any one of the above         (1.) to (23.);     -   (b) passing a nucleic acid through the tunnel gap;     -   (c) detecting the signal produced when a nucleobase of the         nucleic acid interacts with the first and second reagent;     -   (d) from the signal detected in (c), identifying the nucleobase         of the nucleic acid; and     -   (e) repeating steps (b) through (d);     -   (f) from the nucleobases identified in (d), determining the         sequence of the nucleic acid.         (37.) The method of the above (36.), wherein step (c) comprises         detecting an electrical current.

Definitions

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as those commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. The materials, methods and examples are illustrative only, and are not intended to be limiting. All publications, patents and other documents mentioned herein are incorporated by reference in their entirety.

Throughout this specification, the word “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated integer or groups of integers but not the exclusion of any other integer or group of integers.

The term “a” or “an” may mean more than one of an item.

The terms “and” and “or” may refer to either the conjunctive or disjunctive and mean “and/or”.

The term “about” means within plus or minus 10% of a stated value. For example, “about 100” would refer to any number between 90 and 110.

The term “(C₁-C₆) alkyl” refers to saturated linear or branched hydrocarbon structures having 1, 2, 3, 4, 5, or 6 carbon atoms. When an alkyl residue having a specific number of carbons is named, all geometric isomers having that number of carbons are intended to be encompassed; thus, for example, “propyl” includes n-propyl and iso-propyl, and “butyl” includes n-butyl, sec-butyl, iso-butyl and tert-butyl. Examples of “(C₁-C₆) alkyl” include methyl, ethyl, n-propyl, iso-propyl, n-butyl, tert-butyl, n-pentyl, n-hexyl, and the like.

The term “(C₁-C₃) alkyl” refers to saturated linear or branched hydrocarbon structures having 1, 2 or 3 carbon atoms. When an alkyl residue having a specific number of carbons is named, all geometric isomers having that number of carbons are intended to be encompassed; thus, for example, “propyl” includes n-propyl and iso-propyl. Examples of “(C₁-C₃) alkyl” include methyl, ethyl, n-propyl and iso-propyl.

The term “(C₂-C₆) alkenyl” refers to a straight-chain or branched unsaturated hydrocarbon radical having 2, 3, 4, 5 or 6 carbon atoms and a double bond in any position, e.g., ethenyl, 1-propenyl, 2-propenyl (allyl), 1-butenyl, 2-butenyl, 3-butenyl, 1-methylethenyl, 1-methyl-1 propenyl, 2-methyl-2-propenyl, 2-methyl-1-propenyl, 1-methyl-2-propenyl, 1-hexenyl, 2-hexenyl, 3-hexenyl, 2-methyl-2-pentenyl, 4-methyl-2-pentenyl, 4-methyl-1-pentenyl, 3-methyl-1-pentenyl, and the like.

The term “(C2-C6)alkynyl” refers to a straight chain or branched hydrocarbon having 2, 3, 4, 5 or 6 carbon atoms and including at least one carbon-carbon triple bond. Examples of “(C2-C₆)alkynyl” include ethynyl, propynyl, 1-butynyl, 2-butynyl, 1-pentynyl, 2-pentynyl, 1-hexynyl, 2-hexynyl, 3-hexynyl, 4-methyl-2-pentynyl and the like.

The term “(C₁-C₆)alkoxy” refers to —O—(C₁-C₆)alkyl. Examples of “(C₁-C₆)alkoxy” include methoxy, ethoxy, propoxy, n-propoxy, iso-propoxy, butoxy, n-butoxy, sec-butoxy, tert-butoxy, pentoxy, hexyloxy, and the like.

The term “(C₁-C₃)alkoxy” refers to —O—(C₁-C₃)alkyl. Examples of “(C₁-C₆)alkoxy” include methoxy, ethoxy, propoxy, n-propoxy and iso-propoxy.

The term “electron withdrawing group” refers to an atom or group that draws electron density from neighboring atoms towards itself. Examples of “electron withdrawing groups” include halo, —CN, —CF₃, —NO₂, —SH, —C(O)H, —C(O)—(C₁-C₆)alkyl, —C(O)O—(C₁-C₆)alkyl, —C(O)OH, —C(O)—Cl, —SO₂OH, —S(O)₂NHOH, —NH₃, —N((C₁-C₆)alkyl)₃ and the like.

The term “electron donating group” refers to an atom or a group that donates some of its electron density to neighboring atoms. Examples of “electron donating groups” include —OH, —NH₂, —N((C₁-C₆)alkyl)₂, NHC(O)(C₁-C₆)alkyl), —OC(O)(C₁-C₆)alkyl), (C₁-C₆)alkyl), phenyl, —CH═C((C₁-C₆)alkyl))₂ and the like.

The term “Peptide Nucleic Acid” or “PNA” is a non-naturally occurring polymer comprising a polyamide backbone, and purine and pyrimidine bases linked thereto.

The term “Xeno Nucleic Acid” or “XNA” is a non-naturally occurring polymer in which the deoxyribose or ribose groups of DNA and RNA have been replaced. Examples of XNA include, but are not limited to, 1,5-anhydrohexitol nucleic acid (HNA), cyclohexene nucleic acid (CeNA), threose nucleic acid (TNA), glycol nucleic acid (GNA), locked nucleic acid (LNA), and peptide nucleic acid (PNA).

The term “unnatural base” refers to a non-naturally occurring molecule that is incorporated into a nucleic acid and can form a base pair with a natural base or another unnatural base. Unnatural bases are known in the art and examples include, but are not limited to, substituted or unsubstituted 2-aminopurine, substituted or unsubstituted imidazo[4,5-b]pyridine, substituted or unsubstituted pyrrolo[2,3-b]pyridine, substituted or unsubstituted pyridin-2-one, substituted or unsubstituted pyrrole-2-carbaldehyde, and substituted or unsubstituted 2-nitropyrrole, isoguanine, isocytosine, xanthosine, 2,4-diaminopyrimidine, 4-methylbenzimidazole, difluorotoluene, propynyl isocarbostyril, 7-azaindole, and 3-fluorobenzene.

The abbreviation “Py” refers to 1-(2-mercaptoethyl)pyrene.

The abbreviation “Iz” refers to 4(5)-(2-mercaptoethyl)-1H-imidazole-2-carboxamide.

The abbreviation “Bn” refers to (2-mercaptoethyl)benzene.

The abbreviation “RT” refers to Recognition Tunneling.

The abbreviation “AP” refers to abasic 5′-monophosphate.

Apparatus

The present disclosure provides an apparatus for selectively analyzing a nucleic acid sequence in a sample. The sample may be a biological sample and comprises DNA in one embodiment and RNA in a second embodiment. In one embodiment, the sample comprises DNA derived from a patient. The apparatus comprises a chamber, which, in turn comprises two electrodes. The two electrodes form a tunnel gap through which the sample of nucleic acid sequence can pass.

The electrodes may be made of any suitable material that can be functionalized with a reagent capable of interacting with a nucleobase. In one embodiment, the electrode is made from a metal. Suitable metals include palladium, gold, graphene, carbon nanotube, and molybdenum disulphide.

In one embodiment, the electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. In another embodiment, the electrodes comprise an aromatic compound that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. The electrodes may be functionalized with the same reagent in one embodiment, a combination of reagents in a second embodiment, or individually functionalized with different reagents in a third embodiment.

In one embodiment, the reagent comprises a functional group that selectively attaches to the electrode surface. Suitable functional groups include a thiol, disulfide or amine. The selection of a functional group will depend on the electrode used. For example, when the electrode is made from gold or palladium, a reagent comprising a thiol may be used. When the electrode is made from graphene or carbon nanotubes, a reagent comprising an amine may be used.

In embodiments in which the electrode comprises an aromatic compound, the aromatic compound is selected from the group consisting of pyrene, benzene, anthracene, benzo[e]pyrene, 2-(phenylethynyl)pyrene, 2-phenyl pyrene, 3-nitro-1H-pyrrole and 5-nitro-1H-indole, wherein the aromatic compound is unsubstituted or substituted with a substituent selected from the group consisting of nitro, phenyl, (C₁-C₆)alkyl substituted with phenyl, (C₂-C₆)alkenyl substituted with phenyl and (C₂-C₆)alkynyl substituted with phenyl. In one embodiment, the aromatic compound is selected from the group consisting of pyrene, 1-(2-mercaptoethyl)pyrene, nitrobenzene, 2-nitroanthracene, benzo[e]pyrene, 2-(phenyl ethynyl)pyrene, 2-phenyl pyrene, 3-nitro-1H-pyrrole and 5-nitro-1H-indole. In another embodiment, the aromatic compound is a pyrene. In another embodiment, the aromatic compound is a pyrene substituted with (C₁-C₆)mercaptoalkyl. In another embodiment, the aromatic compound is 1-(2-mercaptoethyl)pyrene. In another embodiment, the aromatic compound is a compound of formula (I), as disclosed herein.

The tunnel gap comprises the space between the two electrodes and can be adjusted to a width such that the nucleic acid fits into the gap. The gap width will vary depending on the reagent used and the nucleic acid to be analyzed. The gap may have a width from about 1 nm to about 5 nm, from about 1.5 nm to about 4.5 nm, from about 2 nm to about 4 nm, from about 2 nm to about 3.5 nm, from about 2 nm to about 3 nm, or from about 2 nm to about 2.5 nm.

Methods for determining suitable gap widths are known in the art. For example, U.S. Pat. No. 9,140,682 provides that suitable gap widths may be determined by using a device capable of a dynamically adjusting the gap. In some embodiments, a dynamically adjustable device may be used to analyze target units. In either case, the gap width may be determined or set as follows: The electrodes are approached together until a chosen tunnel current is achieved at a particular bias. For example, a current of 6 pA at 0.5V bias corresponds to a gap of 2.5 nm when tunneling in 1,2,4-trichlorobenzene. The gap is maintained by applying active servo control as is well known in the art for scanning tunneling microscopy.

When the nucleobase is passed through the tunnel gap, it interacts with the first and second reagent via π-π interactions. The interaction between the nucleobase and the reagent produce a detectable signal. The apparatus of this disclosure is highly specific to nucleobases.

In one embodiment, the apparatus further comprises one or more of the following: a detector for measuring the detectable signal, a system for introducing and removing buffer and the sample into the chamber, and a system for analyzing the detectable signal.

Compounds of Formula (I)

The present disclosure provides a compound of formula (I):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; R₂ is selected from the group consisting of thiol, disulfide, and amine; and R₃ is H, an electron withdrawing group or an electron donating group; ring A or ring B is substituted with R₁-R₂; and ring C or ring D is substituted with R₃; provided that (i) when ring A is substituted with R₁-R₂, ring C is substituted with R₃; and (ii) when ring B is substituted with R₁-R₂, ring D is substituted with R₃.

In one embodiment, R₁ is (C₁-C₃) alkyl or (C₁-C₃) alkoxy. In another embodiment, R₁ is (C₁-C₆) alkyl. In another embodiment, R₁ is (C₁-C₆) alkoxy. In another embodiment, R₁ is (C₁-C₃) alkyl.

In another embodiment, R₁ is (C₁-C₃) alkoxy. In another embodiment, R₁ is methyl or ethyl. In another embodiment, R₁ is methyl. In another embodiment, R₁ is ethyl. In another embodiment, R₁ is methoxy or ethoxy. In another embodiment, R₁ is methoxy. In another embodiment, R₁ is ethoxy.

In one embodiment, R₂ is thiol or disulfide. In another embodiment, R₂ is thiol or amine. In another embodiment, R₂ is disulfide or amine. In another embodiment, R₂ is thiol. In another embodiment, R₂ is disulfide. In another embodiment, R₂ is amine.

In one embodiment, R₃ is H or an electron withdrawing group. In another embodiment, R₃ is H or an electron donating group. In another embodiment, R₃ is an electron withdrawing group or an electron donating group. In another embodiment, R₃ is H. In another embodiment, R₃ is an electron withdrawing group. In another embodiment, R₃ is an electron donating group. In another embodiment, R₃ is H, —NO₂, or —CH₃. In another embodiment, R₃ is H or —NO₂. In another embodiment, R₃ is H or —CH₃. In another embodiment, R₃ is —NO₂ or —CH₃. In another embodiment, R₃ is —NO₂. In another embodiment, R₃ is —CH₃.

In one embodiment, R₁ is (C₁-C₃) alkyl and R₂ is thiol. In another embodiment, R₁ is (C₁-C₃) alkyl and R₂ is disulfide. In another embodiment, R₁ is (C₁-C₃) alkyl and R₂ is amine. In another embodiment, R₁ is methyl and R₂ is thiol. In another embodiment, R₁ is methyl and R₂ is disulfide. In another embodiment, R₁ is methyl and R₂ is amine. In another embodiment, R₁ is ethyl and R₂ is thiol. In another embodiment, R₁ is ethyl and R₂ is disulfide. In another embodiment, R₁ is ethyl and R₂ is amine.

In one embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is thiol. In another embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is disulfide. In another embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is amine. In another embodiment, R₁ is methoxy and R₂ is thiol. In another embodiment, R₁ is methoxy and R₂ is disulfide. In another embodiment, R₁ is methoxy and R₂ is amine. In another embodiment, R₁ is ethoxy and R₂ is thiol. In another embodiment, R₁ is ethoxy and R₂ is disulfide. In another embodiment, R₁ is ethoxy and R₂ is amine.

The present disclosure also provides a compound of formula (Ia):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; and R₂ is selected from the group consisting of thiol, disulfide, and amine.

In one embodiment, R₁ is (C₁-C₃) alkyl or (C₁-C₃) alkoxy. In another embodiment, R₁ is (C₁-C₆) alkyl. In another embodiment, R₁ is (C₁-C₆) alkoxy. In another embodiment, R₁ is (C₁-C₃) alkyl. In another embodiment, R₁ is (C₁-C₃) alkoxy. In another embodiment, R₁ is methyl or ethyl. In another embodiment, R₁ is methyl. In another embodiment, R₁ is ethyl. In another embodiment, R₁ is methoxy or ethoxy. In another embodiment, R₁ is methoxy. In another embodiment, R₁ is ethoxy.

In one embodiment, R₂ is thiol or disulfide. In another embodiment, R₂ is thiol or amine. In another embodiment, R₂ is disulfide or amine. In another embodiment, R₂ is thiol. In another embodiment, R₂ is disulfide. In another embodiment, R₂ is amine.

In one embodiment, R₁ is (C₁-C₃) alkyl and R₂ is thiol. In another embodiment, R₁ is (C₁-C₃) alkyl and R₂ is disulfide. In another embodiment, R₁ is (C₁-C₃) alkyl and R₂ is amine. In another embodiment, R₁ is methyl and R₂ is thiol. In another embodiment, R₁ is methyl and R₂ is disulfide. In another embodiment, R₁ is methyl and R₂ is amine. In another embodiment, R₁ is ethyl and R₂ is thiol. In another embodiment, R₁ is ethyl and R₂ is disulfide. In another embodiment, R₁ is ethyl and R₂ is amine.

In one embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is thiol. In another embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is disulfide. In another embodiment, R₁ is (C₁-C₃) alkoxy and R₂ is amine. In another embodiment, R₁ is methoxy and R₂ is thiol. In another embodiment, R₁ is methoxy and R₂ is disulfide. In another embodiment, R₁ is methoxy and R₂ is amine. In another embodiment, R₁ is ethoxy and R₂ is thiol. In another embodiment, R₁ is ethoxy and R₂ is disulfide. In another embodiment, R₁ is ethoxy and R₂ is amine.

In one embodiment, the compound of formula (Ia) is a 1-pyrene, 2-pyrene or 4-pyrene. In another embodiment, the compound of formula (Ia) is 1-pyrene or 2-pyrene. In another embodiment, the compound of formula (Ia) is 1-pyrene or 4-pyrene. In another embodiment, the compound of formula (Ia) is 2-pyrene or 4-pyrene. In another embodiment, the compound of formula (Ia) is 1-pyrene. In another embodiment, the compound of formula (Ia) is 2-pyrene. In another embodiment, the compound of formula (Ia) is 4-pyrene.

Methods of Use

The present disclosure also provides a method of determining the sequence of a nucleic acid. The method comprises providing an apparatus as disclosed herein. A sample comprising a nucleic acid is passed through the tunnel gap either by diffusion or through electrophoresis. When a nucleobase of the nucleic acid passes through the tunnel gap it interacts with the first and second reagent. This interaction is due to π-π stacking between the first and second reagent (e.g., the aromatic compound) and the nucleobase. The π-π stacking interaction causes a detectable signal. The signal so produced can be detected, e.g., by detecting an electrical current. From the detectable signal, the nucleobase is identified. These steps are repeated until the sequence of the nucleic acid is determined. The nucleic acid is DNA in one embodiment and RNA in a second embodiment.

In one embodiment, the method of determining the sequence of a nucleic acid further comprises providing a second apparatus comprising a third and a fourth electrode that form a tunnel gap through which the nucleic acid sequence can pass, wherein the third and the fourth reagents are each capable of forming a transient bond to a nucleobase of the nucleic acid, the third and fourth reagents being independently selected from the group consisting of mercaptobenzoic acid, 4-mercaptobenzcarbamide, imidazole-2-carboxide, and 4-carbamonylphenyldithiocarbamate. A sample comprising a nucleic acid is passed through the tunnel gap either by diffusion or through electrophoresis. When the transient bond between the third and fourth reagent and the nucleobase forms, a detectable signal is produced. From this detected signal, the nucleobase is identified.

These steps are repeated until the sequence of the nucleic acid is determined. The nucleic acid is DNA in one embodiment and RNA in a second embodiment.

In embodiments in which the nucleic acid is sequenced using two apparatus, the first and second reagent are as defined in herein and the third and fourth reagent is 4(5)-(2-mercaptoethyl)-1H-imidazole-2-carboxamide (Iz). In one embodiment, the first and second reagent is 1-(2-mercaptoethyl)pyrene (Py) and the third and fourth reagent is 4(5)-(2-mercaptoethyl)-1H-imidazole-2-carboxamide (Iz).

The two apparatus sequencing method provides comprehensive information on genome sequences, for example, damages of DNA bases, which is lost in NGS due to the use of polymerases that can incorporate dAMP into the opposite of an abasic site (“A rule”³⁸) or cause a frameshift.³⁹

In order that this invention be more fully understood, the following examples are set forth. These examples are for the purpose of illustration only and are not to be construed as limiting the scope of the invention in any way.

EXAMPLES Example 1—Computer Modeling

To demonstrate the feasibility to distinguish between two DNA bases via the π-π, a computer model comprising two gold electrodes each carrying a Py molecule through a S—Au bond, which sandwiches a uracil base (FIG. 2) or a guanine base (FIG. 3) was built. With the gold atoms fixed, the system geometry is optimized to the minimum energy using Self-Consistent Charge-Tight-Binding DFT (SCC-DFTB) (Aradi B et al., J. Phys. Chem. A 2007, 111: 5678-5684; Elstner M, et al. Physical Review B 1998, 58(11): 7260-7268; and Porezag D, et al. Physical Review B 1995, 51(19): 12947-12957). Using first-principles quantum transport simulations, based on the nonequilibrium Green function formalism (Datta S. Nanotechnology 2004, 15(7): S433-S451) combined with tight-binding density functional theory (NEGF+DFTB) (Pecchia A, Carlo A D. Reports on Progress in Physics 2004, 67(8): 1497-1561; Pecchia A, et al., New Journal of Physics 2008, 10(6): 065022; and Carlo A D et al., In: Cuniberti G, Fagas G, Richter K (eds). Introducing Molecular Electronics: Lecture Notes in Physics vol. 680. Springer: Berlin, 2005, pp 153-184), changes in the total electronic currents through the system were examined with the voltage bias U applied between the gold electrodes in a range of 0.01 V to 1 V.

As shown in FIG. 4, at lower bias the current response is a weak power dependence on the voltage (up to ˜0.2 V for guanine, and to ˜0.4 V for uracil), which can be fit to an analytic form I=G(U/U₀)^(b), where for uracil G=307 pA, b=1.03 and for guanine G=1.228.6, b=1.24. In both cases U₀=1 V. This example shows that a sandwiched complex of pyrene with a purine (guanine) ring is more conductive than with the pyrimidine (uracil) in a nanogap when they are optimally stacked without steric hindrance.

Example 2—Synthesis General Information.

Reagents and solvents were purchased from commercial suppliers (Sigma-Aldrich, Alfa Aesar, Fluka, TCI America) and used as received unless otherwise noted. All experiments requiring anhydrous conditions were performed in flame-dried glassware under nitrogen atmosphere. Reactions were monitored by thin layer chromatography (TLC) using glass plates pre-coated with silica gel (EMD Chemicals Inc.). Flash chromatography was performed in an automated flash chromatography system (CombiFlash R_(f), Teledyne Isco, Inc.) with silica gel columns (60-120 mesh). Purchased 1-bromopyrene (95% purity) was further purified by silica gel flash chromatography eluting with hexane, dried at 40° C. overnight, and stored over drierite under vacuum. THF was freshly distilled over sodium prior to use. Nitrogen was flowed through drierite before it went into the reaction vessel. Ethylene oxide (1.2 M solution in dichloromethane) was stored over molecular sieves for two days before use. ¹H NMR and ¹³C NMR spectra were recorded on Varian INOVA 400 (400 MHz) and Varian INOVA 500 (500 MHz) spectrometers at 25° C. at the Magnetic Resonance Research Center at Arizona State University. Chemical shifts (6) are given in parts per million (ppm) and are referenced to the residual solvent peak (CDCl₃: δ_(H)=7.26 ppm, CD₃OD: δ_(H)=3.31 ppm, DMSO-d₆: δ_(H)=2.50 ppm). Coupling constants (1) are expressed in hertz (Hz) and the values are rounded to the nearest 0.1 Hz. Splitting patterns are reported as follows: br, broad; s, singlet; d, doublet; dd, doublet of doublets; t, triplet; dt, doublet of triplets; q, quartet and m, multiplet. High resolution mass spectra (HRMS) are acquired at the Arizona State University CLAS High Resolution Mass Spectrometry Facility.

2.1—1-(2-mercaptoethyl)pyrene (Py)

1-(2-mercaptoethyl)pyrene (Py) can be synthesized according to Scheme 1.

2-(pyren-1-yl)ethanol (1)

A solution of 1-bromopyrene (0.4 g, 1.42 mmol in 12 mL THF) was added onto magnesium turnings (0.1 g, 4.27 mmol) in a flame-dried Schlenk flask. It was refluxed at 70° C. while the solution turned into dark brown color and continued to reflux for another 2 h. The resulting solution was cooled in an ice bath followed by addition of ethylene oxide solution (3.6 mL, 4.27 mmol in 6 mL THF). The mixture was allowed to warm to room temperature and stirred for 12 h. It was cooled in an ice bath then hydrolyzed by careful addition of HCl (5 mL 10%), and extracted with ethyl acetate (20 mL×2). The combined organic layers were washed with brine (40 mL), dried over MgSO₄, filtered and evaporated to dryness by rotary evaporator. The product was separated through a silica gel column by flash chromatography using a gradient of ethyl acetate (0-20% for 3 h) in hexane. Compound 1 was obtained as yellow solid (0.14 g, 40%). ¹H NMR (500 MHz, CDCl₃): δ 8.29 (d, J=9.0 Hz, 1H, ArH), 8.18 (d, J=8.0 Hz, 2H, ArH), 8.10-8.13 (m, 2H, ArH), 7.99-8.04 (m, 3H, ArH), 7.90 (d, J=8.0 Hz, 1H, ArH), 5.29 (s, br, 1H, OH), 4.09 (t, J=6.5 Hz, 2H, CH ₂CH₂OH), 3.61 ppm (t, J=6.5 Hz, 2H, CH₂CH ₂OH); ¹³C NMR (125 MHz, CDCl₃): δ 132.53, 131.52, 130.98, 130.42, 129.36, 128.07, 127.68, 127.57, 127.09, 126.06, 125.24, 125.19, 125.00, 124.97, 123.30, 63.95, 36.78, 29.85 ppm; HRMS (FAB+): found m/z 247.1129; calculated for C₁₈H₁₄O+H: 247.1123.

2-(Pyren-1-yl)ethyl 4-methylbenzenesulfonate (2)

Triethyl amine (0.08 mL, 0.55 mmol) was added into a solution of compound 1 (45 mg, 0.18 mmol) and tosyl chloride (52 mg, 0.28 mmol) in dichloromethane (1.5 mL) at room temperature. The resulting solution was stirred for 16 h, followed by addition of saturated sodium bicarbonate solution (5 mL), extracted with dichloromethane (3×10 mL). The combined organic phase was washed with brine (30 mL), dried over MgSO₄, filtered and evaporated to dryness by rotary evaporator. The residue was separated by flash chromatography through a silica gel column using a gradient of ethyl acetate (0-20% over a period of 3 h) in hexane. Compound 2 was obtained as a white solid (60 mg, 82%). ¹H NMR (500 MHz, CDCl₃): δ 8.17 (q, J=7.5 Hz, 2H, ArH), 7.95-8.05 (m, 6H, ArH), 7.76 (d, J=7.5 Hz, 1H, ArH), 7.33 (d, J=8.5 Hz, 2H, Tosyl-ArH), 6.68 (d, J=8.5 Hz, 2H, Tosyl-ArH), 4.44 (t, J=7.0 Hz, 2H, CH ₂CH₂OTs), 3.64 (t, J=7.0 Hz, 2H, CH₂CH ₂OTs), 1.85 ppm (s, 3H, CH ₃); ¹³C NMR (125 MHz, CDCl₃): δ 144.28, 132.23, 131.37, 130.73, 130.65, 129.96, 129.17, 128.87, 128.28, 127.80, 127.47, 127.40, 127.23, 126.08, 125.32, 125.13, 125.06, 124.80, 124.79, 122.52, 70.41, 33.06, 21.05 ppm; HRMS (FAB+): found m/z 401.1213; calculated for C₂₅H₂₀O₃S+H 401.1211.

S-(2-(pyren-1-yl)ethyl) ethanethioate (3)

Potassium thioacetate (24 mg, 0.206 mmol) was added to a solution of 2 (55 mg, 0.138 mmol) in DMF (1.5 mL). The resulting mixture was stirred for 16 h at room temperature. Brine (10 mL) was added into the reaction mixture, extracted with dichloromethane (2×10 mL). The combined organic phase was dried over MgSO₄, filtered and evaporated to dryness by rotary evaporator. The residue was separated by flash chromatography through a silica gel column using a gradient of ethyl acetate (0-5% over 3 h) in hexane. Compound 3 was obtained as a white solid (32 mg, 78%). ¹H NMR (500 MHz, CDCl₃): δ 8.42 (d, J=9.5 Hz, 1H, ArH), 8.15-8.20 (m, 3H, ArH), 8.12 (d, J=8.0 Hz, 1H, ArH), 7.99-8.04 (m, 3H, ArH), 7.89 (d, J=8.0 Hz, 1H, ArH), 3.60 (t, J=8.0 Hz, 2H, CH ₂CH₂S), 3.32 (t, J=8.0 Hz, 2H, CH₂CH ₂S), 2.41 ppm (s, 3H, CH ₃); ¹³C NMR (125 MHz, CDCl₃): δ 196.17, 134.14, 131.49, 131.00, 130.47, 129.01, 127.86, 127.56, 127.10, 126.02, 125.18, 125.17, 125.06, 125.03, 124.96, 123.30, 33.95, 30.87 ppm (two carbons were not identified); HRMS (FAB+): found m/z 305.1001; calculated for C₂₀H₁₆OS+H: 305.1000.

1-(2-Mercaptoethyl)pyrene (Py)

Pyrrolidine (2 μL, 24.6 μmol) was added into a solution of 3 (5 mg, 16.4 μmol) in ethanol (1 mL) and stirred for 30 min at room temperature. Solvent was evaporated to dryness by rotary evaporator to obtain Py (4.3 mg, 100%). R_(f) on TLC: 0.18 (9:1 hexane/ethyl acetate). HRMS (APCI+): found m/z 263.0886; calculated for C₁₈H₁₄S+H: 263.0894.

Example 3—Monolayers on Palladium Substrates 3.1 Fabrication of Palladium Substrate

Palladium substrates were made in ASU CSSER cleanroom using Lesker PVD75 Electron Beam Evaporator (Lesker#3). Pure (99.99%) palladium and titanium metal targets were bought from Kurt J. Lesker Company and circular silicon wafers (10 cm diameter) were purchased from Silicon Quest International. Prior to use, silicon wafers were cleaned with hydrofluoric acid, washed with isopropanol and nanopure water, and then blow-dried with a nitrogen flow. Over the silicon wafer a thin titanium adhesive layer (10 nm thick) was deposited, and then a palladium layer (200 nm thick) was deposited over titanium film. Small squares of 1×1 cm² were cut prior to use.

3.2 Monolayer Formation and Characterization

3.2.1 Rp Monolayer.

A solution of compound 3 (100 μM) and pyrrolidine (1 mM) in degassed ethanol was prepared. After 30 min, a palladium substrate was immersed into the solution for 8 h, washed thoroughly with ethanol, dried with a nitrogen flow, used immediately for the RT measurement. The Py monolayer was characterized with elliposometry and contact angle (Table 1), XPS (FIG. 5), and FTIR (FIG. 6A and FIG. 6B).

TABLE 1 Thickness* and contact angle** of Py SAM on palladium Thickness by Ellipsometry (Å) Contact Angle (°) 8.6 ± 0.6 67.8 ± 4.5 *Thickness of self-assembled monolayers were measured using LSE STOKES Ellipsometer with HeNe laser (632.8 nm wavelength) and 70° incident angle of measuring laser beam. Prior to functionalization, ellipsometric parameters of bare clean palladium substrates were measured and used for determination of the thickness of the functionalized substrates. Refractive index value for organic film was set as 1.46. **Easydrop was used to measure water contact angle of SAM on palladium substrate. The data reported is an average of five measurements taken on each sample with a water droplet volume of 1 μL.

For the XPS experiments, X-ray photoelectron spectra were obtained using Al-Kα radiation (15 keV) at 6×10-10 mbar base pressure on VG ESCALAB 220i-XL photoelectron spectrometer. Wide scan spectra were recorded at 150 eV pass energy and high resolution spectra for C(1s), Pd(3d) and S(2p) were obtained at 20 eV pass energy. Elemental ratio of the SAM was calculated from area under the peaks of corresponding elements using CasaXPS software package. Table 2 shows the calculated elemental ratio and experimental elemental ratio.

TABLE 2 Calculated Elemental Ratio Found Elemental Ratio for S:C for S:C (from XPS) 1:18 1:13.5 ± 1.5

For the FTIR experiments, the spectrum of powder sample was acquired with smart orbit (attenuated total reflection) and SAM spectra with SAGA (Specular Aperture Grazing Angle) accessory on Thermo Nicolet A Nicolet 6700 FT-IR (Thermo Electron CoPyoration) instrument equipped with a MCT detector. A background spectrum was recorded before recording the FT-IR of powder sample and a bare palladium substrate in case of monolayer sample. All the FT-IR data were subjected to baseline correction using the built-in spectrum program. The vibration around 3040 cm⁻¹ is assigned to aromatic C—H stretching, 1600 cm⁻¹ aromatic C—C stretching, ˜2925 cm⁻¹ aliphatic C—H stretching from methylene group, and 2564 cm⁻¹ s-h stretching, which is absent in SAM spectrum.

3.2.2 Bn Monolayer.

A palladium substrate was immersed in an ethanolic (degassed) solution of Bn (50 μM) for 2.5 h, washed thoroughly with ethanol, dried with a nitrogen flow, and used immediately. The Bn monolayer was characterized with elliposometry and contact angle following the protocol of Example 3.2.1 (Table 3), and FTIR (FIG. 7A and FIG. 7B)

TABLE 3 Thickness and contact angle of Bn SAM on palladium Thickness by Ellipsometry (Å) Contact Angle (°) 5.6 ± 0.9 79.5 ± 4.1

For the FTIR experiments, the spectrum of neat sample was acquired with SMART ORBIT (Attenuated Total Reflection) and SAM spectra with SAGA (Specular Aperture Grazing Angle) accessory on Thermo Nicolet A Nicolet 6700 FT-IR (Thermo Electron CoPyoration) instrument equipped with a MCT detector. A background spectrum was recorded before recording the FTIR of powder sample and a bare Palladium substrate in case of monolayer sample. All the FTIR data were subjected to baseline correction using the built-in SPECTRUM program. The vibration around 3020 cm“¹ is assigned to aromatic C—H stretching, 1600 cm”¹ aromatic C—C stretching, 2930 cm“¹ aliphatic C—H stretching from methylene group, and 2568 cm”¹ S—H stretching, which is absent in the SAM spectrum.

3.2.3 Iz monolayer. A palladium substrate was immersed in a degassed ethanolic solution of Iz (500 μM) for 16 h, washed thoroughly with ethanol, dried with a nitrogen flow, and used immediately (Chang S, et al. Nanotechnology 23, 425202 (2012).

This example confirms by contact angle measurements, elliposometry, FTIR and XPS that both Py and Bn could form monolayers on the Pd substrate.

Example 4—Surface Plasmon Resonance (SPR)

A gold chip was immersed into an absolute ethanol solution of Py (100 μM) for 24 h, followed by rinsing with absolute ethanol and drying with a nitrogen flow, and used immediately. The instrument Bi 2000 from Biosensing Instrument was used for SPR measurements. The Py modified gold chip was mounted on the instrument and calibrated with 1% ethanol in a phosphate buffer, pH 7.4. A solution of dGMP (500 μM) was flowed onto the chip at a rate of 50 ul/min over a period of 1.5 min, followed by flowing the PBS buffer (FIG. 8). Association (k_(on)) and dissociation rate constants (k_(off)) were determined using built-in Biosensing Instrument SPR data analysis software version 2.4.6 (Table 4).

TABLE 4 Kinetic parameters of dGMP on the pyrene monolayer¹ k_(on) k_(off) K_(d) ² dGMP (M⁻¹ s⁻¹) (s⁻¹) (mM) Res. sd 500 uM 41.60 ± 2.50 0.09 ± 0.01 2.46 ± 0.12 10.2 ± 1.4 4 ¹Each datum listed is an average of three measurements. ²K_(d) = k_(off)/k_(on)

This example shows that DNA nucleotides, such as dGMP, can be absorbed on the Py monolayer with an affinity of K_(d)=2.46 mM in aqueous solution.

Example 5—Recognition Tunneling Using Scanning Tunneling Microscope

In this example, Scanning Tunneling Microscope (STM) was used to create the tunnel nanogaps between a Pd probe and a Pd substrate, which were functionalized with Py, Iz, or a (2-mercaptoethyl)benzene control (Bn) prior to use.

5.1 DNA Monophosphate Solution

All of analytes were purchased from Sigma Aldrich with purity of ≥98% except AP (≥95%). Ultrapure water with specific resistance ˜18 MΩ and organic carbon particle ˜4 ppb from MilliQ system was used for solution preparation. Each analyte solution was prepared with a concentration of 100 μM in 1.0 mM phosphate buffer pH 7.4.

5.2 Functionalization of Palladium Probes

Probe preparation followed a known method.¹⁰ A batch of 4 STM Probes were made by electrochemically etching palladium wires with 0.25 mm diameter (from California Fine Wires) in a mixed solution of con. hydrochloric acid (36% w) and ethanol (1:1), followed by insulation with high density polyethylene, which left the apexes open. Any probes with leakage current >1 pA were discarded and the rest were functionalized with reader molecules in the same procedure as described above for preparation of the monolayer, and used immediately for RT measurements.

5.3 Data Collection

The measurements were carried out in PicoSPM instrument (Agilent Technologies), interfaced with a customized Labview program. The sampling rate for tunnel current was 50 kHz. Prior to the experiment, the STM teflon cell was cleaned with piranha followed by vigorous rinsing with Milli-Q water and ethanol. After adding ˜150 μL, of phosphate buffer (1.0 mM, pH 7.4 to the STM cell, the functionalized palladium probe (with leakage current <1 pA; prepare in Example 5.2) and Pd-substrate were installed to the scanner. The probe was approached to the functionalized surface with a set value of 1.0 for integral and proportional gain at 2 pA current set-point and −500 mV bias (substrate negative). Then a few images were scanned to ensure that the probe was perfectly oriented over the substrate and the Pd crystal grains can be seen clearly. After that, the probe was withdrawn for 10 μm for 2 hours to ensure that there was no drift and minimal mechanical noise. Then the probe was engaged again and control data was recorded with a reduced value of integral and proportional gain (0.1). Once the tunnel junction was stabilized, the phosphate buffer was discarded and an analyte solution was introduced (typically 100 μM in 1.0 mM phosphate buffer, pH 7.4) to the liquid cell, and current recordings were collected under a predefined tip-substrate bias. Four naturally occurring DNA nucleoside monophosphates (dAMP, dCMP, dGMP and dTMP) and two sugar molecules (abasic 5′-monophosphate, designated as AP, and D-glucose) were used as analytes (prepared in Section 5.1). For each analyte, four data sets were collected separately with freshly made probes, substrates, and samples.

5.4 Data Analysis

5.4.1 Analysis of RT Raw Data

Data recorded in the time domain was characterized by peak height, averaged amplitude, peak width, and so on (FIG. 9A) using Matlab. First, the baseline tunneling current was shifted to zero and a threshold was set at 15 pA for the tunneling spikes. The peak height, averaged amplitude, peak width data were exported to OriginPro 2016 for the analysis and the curve fitting was performed using the built-in Levenberg-Marquardt algorithm. The results are listed in Table 5.

TABLE 5 Py Iz dAMP dCMP dGMP dTMP Average dAMP dCMP dGMP dTMP Average Peak Median 9.74 ± 8.29 ± 8.92 ± 8.97 ± 8.98 ± 8.46 ± 8.88 ± 8.81 ± 8.75 ± 8.72 ± height 0.06 0.03 0.04 0.03 0.02 0.03 0.03 0.03 0.03 0.02 (pA) Mean 10.34 ± 8.52 ± 9.28 ± 9.33 ± 9.36 ± 8.69 ± 9.16 ± 9.10 ± 9.02 ± 8.99 ± 0.07 0.03 0.05 0.04 0.02 0.03 0.04 0.03 0.03 0.02 Sigma 3.67 ± 2.03 ± 2.65 ± 2.67 ± 2.76 ± 2.03 ± 2.35 ± 2.35 ± 2.29 ± 2.26 ± (σ) 0.08 0.04 0.05 0.04 0.03 0.03 0.04 0.04 0.03 0.02 Averaged Median 4.64 ± 4.87 ± 4.41 ± 3.62 ± 4.39 ± 4.38 ± 4.45 ± 4.32 ± 4.33 ± 4.37 ± amplitude 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 (pA) Mean 4.91 ± 5.02 ± 4.62 ± 3.89 ± 4.61 ± 4.57 ± 4.67 ± 4.54 ± 4.54 ± 4.58 ± 0.02 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Sigma 1.68 ± 1.26 ± 1.45 ± 1.52 ± 1.48 ± 1.37 ± 1.48 ± 1.45 ± 1.43 ± 1.43 ± (σ) 0.02 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Peak Median 0.378 ± 0.413 ± 0.395 ± 0.349 ± 0.384 ± 0.404 ± 0.404 ± 0.398 ± 0.404 ± 0.403 ± width 0.002 0.001 0.002 0.001 0.001 0.002 0.002 0.003 0.002 0.001 (ms) Mean 0.397 ± 0.430 ± 0.412 ± 0.360 ± 0.400 ± 0.421 ± 0.421 ± 0.414 ± 0.420 ± 0.419 ± 0.002 0.001 0.003 0.001 0.001 0.003 0.003 0.003 0.003 0.002 Sigma 0.126 ± 0.125 ± 0.124 ± 0.090 ± 0.116 ± 0.123 ± 0.125 ± 0.119 ± 0.120 ± 0.122 ± (σ) 0.002 0.001 0.003 0.001 0.001 0.003 0.003 0.003 0.003 0.002 Spike 7.5 ± 8.1 ± 8.1 ± 12.4 ± 9.0 ± 6.9 ± 8.4 ± 8.9 ± 6.2 ± 7.6 ± Frequency 2.2 2.2 4.7 3.0 1.7 2.5 6.4 3.3 0.8 1.9

5.4.2 Extraction of Features from RT Data

RT spectrum were analyzed by defining two types of event signals, spikes and clusters. The spike is an individual single RT spectrum and the cluster is a subset of close spikes. To define a cluster, a Gaussian window (4096 data points and one-unit height) was applied to the center of each spike.

Spike that lie within a region where the sum of the Gaussian windows continuously exceeds 0.1 were identified as belonging to a cluster. Although each independent tunneling spike was identified by having amplitude above 15 pA, the cluster includes all the spikes within the defined region. Table 6 lists features used to describe an individual spike.

TABLE 6 Feature Name Feature Description Primary P_max Amplitude Maximum amplitude of the peak Features P_average Amplitude Average current of the peak P_top Average Average of the peak above half maximum P_peakWidth Full width at half maximum P_roughness Standard deviation of the peak above half maximum height P_frequency Number of peaks per millisecond over a window of 4096 C_peaksInCluster Number of peaks in the cluster C_frequency Number of peaks in cluster divided by millisecond length of cluster C_average Amplitude Average amplitude of all cluster peaks C_top Average Average amplitude of all peaks above half maximum C_cluster Width Cluster time length in millisecond C_roughness Standard deviation of whole cluster signal C_max Amplitude Average of the max of all the peaks in cluster Secondary P_totalPower Square root of the sum of power spectrum Features P_iFFTLow Average of the first three frequency bands P_iFFTMedium Average of the middle three frequency bands P_iFFTHigh Average of the highest three frequency bands P_peakFFT1~10 Downsampled FFT spectrum P_highLow_Ratio Ratio of P_iFFTLow to P_iFFTHigh P_Odd_FFT Sum of all odd frequencies from the non- downsampled FFT P_Even FFT Sum of all even frequencies from the non- downsampled FFT P_OddEvenRatio Ratio of the odd to the even FFT sums P_peakFFT_Whole1~51 Downsampled FFT spectrum into various bandwidths. (Lower frequency range, smaller bandwidth size) C_totalPower Square root of the sum of the power spectrum C_iFFTLow Average of the first three frequency bands C_iFFTMedium Average of the middle three frequency bands C_iFFTHigh Average of the highest three frequency bands C_clusterFFT1~61 Downsampled FFT spectrum of cluster C_highLow Ratio of the odd to the even FFT sums of cluster C_freq_Maximum_Peak1~4 Frequency of the four dominant peaks in the spectrum, ordered by the height of the peaks C_clusterCepstrum1~61 Spectrum of the power spectrum of the cluster, downsampled to 61 points C_clusterFFT_Whole1~51 Downsampled FFT spectrum into various bandwidths. (Lower frequency range, smaller bandwidth size)

Spikes and clusters in a RT spectrum were Fourier transformed into a 25 kHz window that is the Nyquist frequency of amplifier, and then the whole frequency range was downsampled to small windows. As shown in FIG. 9B, peakFFT and clusterFFT denote features that have the same sampling window size (marked in red), and peakFFT Whole and clusterFFT Whole denote features with varied window size (marked in green), of which those with lower frequencies have smaller sampling window sizes (only six windows are shown for the simplicity in the figure). Furthermore, logarithm of the Fourier transformed spectrum was subjected to the inverse Fourier transform, which generated a new spectrum, referred to as cepstrum (FIG. 9C). The cepstrum was also down sampled into the even size of windows for sampling. Once all the features were determined, they were normalized and scaled to make standard deviation to 1. In this way, features of large numeric values were prevented from dominating those that have small numeric values.

5.4.3 Feature Selection

Randomly selected 10% data were used to construct support vectors (hyper plane to separate analyte data points) to train the SVM, and then tested the rest 90% of data to determine the calling accuracy for each DNA nucleotide. There are totally 264 features in Table 6. Some of them are strongly correlated with one another so they were removed through the normalized correlation calculation between feature pairs with coefficient larger than 0.7. A feature variation between the repeated experiments and different analytes are calculated by comparing histograms of a feature in a single measurement with the accumulated measurements. The difference between the repeated runs histogram and the accumulated histogram of an analyte is assigned as ‘in-group’ fluctuation (variation of the repeats). The difference of a feature between the normalized histogram of a pairs of analytes is ‘out-group’ fluctuation (variation of the analytes). The features were ranked by the ratio between the in-group fluctuation and the out-group fluctuation, and the low ranked features were dropped. The survived features were further optimized to get the maximum true positive accuracy.

5.4.4 SVM Analysis

The kernel-mode SVM available from the world wide web (www) at github.com/vjethava/svm-theta was used. The WM running parameters C and gamma were optimized through cross-validation of randomly selected sub data set. Full details of the SVM (written in Matlab) can be found on the world wide web (www) at github.com/ochensati/SVM_DNA_TunnelVision.

5.5 RT of AP

RT spectra of AP were collected in the same way as we collected those of the DNA nucleotides. A solution (100 μM) of AP (purchased from Sigma Aldrich) was prepared in phosphate buffer, pH 7.4. The RT measurements with Py were carried out at a set point of 2 pA current and 500 mV probe bias, and those with Iz at a set-point of 4 pA current and 500 mV probe bias, always following a clean baseline achieved with pure phosphate buffer.

While RT signals were generated with Iz and four sets of data were recorded for SVM analysis, no AP signal was obtained from the RT experiments with Py as expected. The SVM could not separate the AP signals from those of DNA nucleotides generated by Iz. However, the AP signals appear to be much different from the DNA nucleotide signals generated with Py. As a result, all five analytes (dAMP, dCMP, dGMP, dTMP & AP) were classified with high accuracy (Table 7).

TABLE 7 dAMP dCMP dGMP dTMP AP (Py) (Py) (Py) (Py) (Iz) Average Accuracy 98.4 98.7 97.9 97.0 97.2 97.8

5.6 Results

It was first determined that at the set point of 4 pA and 0.5 V the tunneling gap functionalized with Iz presented a clean baseline. Under these conditions, the gap distance was estimated to be ˜2.4 nm.³⁵ As expected, Iz produced RT signals (spikes) with all of the analytes we measured (FIG. 11A-FIG. 11F). On changing the set-point to 2 pA and 0.5 V, corresponding to a larger gap size, Iz still read the analytes, but less frequently than at 4 pA (data not shown). In contrast, Py presented a cleaner baseline at 2 pA, 0.5 V than at 4 pA, 0.5 V, and it generated RT signals with all the four naturally occurring DNA nucleoside monophosphates, but not with the AP and sugar molecules (FIG. 11G-FIG. 11L). The results may be best explained as a consequence of the formation of sandwiched structures between pyrenes and DNA bases.³⁶ Under the same set point of 2 pA and 0.5 V, the Bn control didn't generate any tunneling signals from these nucleotides (FIG. 11M-FIG. 11P). When the tip was functionalized with Bn and the substrate with Py, interestingly, the purine nucleotides dAMP and dGMP gave tunneling signals, but the pyrimidine nucleotides did not (FIG. 11Q-FIG. 11T). This indicates that the stacking interactions in the tunneling gap are driven by the size of reader molecules, given that Bn is smaller than Py in size, but more polar and hydrophobic. These data show that Py can distinguish DNA nucleotides from other species more effectively than Iz.

RT data were typically collected in the time domain. Each spike in RT spectra was characterized by peak height (in picoamps, pA), averaged amplitude (pA)—an average of all individual current points constituting a spike, and peak width (in milliseconds, ms). The distributions of these parameters for DNA nucleotides acquired using both Py and Iz are plotted in histograms along with their fitting curves respectively (FIG. 12A-FIG. 12F, see Example 5.4.1, for details on analysis of RT raw data). These data were well fit into a Log-Normal function (adjusted R² >94%), from which mean, median and sigma (σ) of those parameters were derived (Table 5). The results show that the tunnel gap functionalized with Py has a marginally higher tunneling current (9.4 pA) with the DNA nucleotides than the one with Iz (9.0 pA) on average for the four nucleotides although the former had a smaller set-point current (2 pA) than the latter (4 pA), but their averaged heights for both Py and Iz are about same (see FIG. 12A-FIG. 12F and Table 5 for the averaged amplitude). The residence time of peaks for Iz with a mean value of 0.42 ms is distributed among the nucleotides more uniformly than those for Py with a mean value of 0.40 ms (FIG. 12A-FIG. 12F and Table 5), an indication that the hydrogen bonding reader is less discriminatory to the nucleobases than the stacking reader. The most important is that the Py reader resolves the four DNA nucleotides with distinguishable modes (FIG. 12A-FIG. 12F) although significant overlaps remain in the distributions. Also, Py can generate tunneling spikes as frequently as Iz with dAMP, dMP and dGMP, and much more frequently with dTMP than Iz (Table 5). This suggests that the π-π stacking is as efficient as the hydrogen bonding for reading DNA bases. However, none of these parameters alone are sufficient to identify the DNA nucleotides.

In order to call DNA nucleotides from the tunneling current data, the RT spectra were subjected to Fourier transform and cepstrum conversion (Example 5.4.2 and FIG. 9A-FIG. 9C), which resulted in 264 features (listed in Table 6). As shown in FIG. 10A-FIG. 10X, no single feature has a capability to distinguish two DNA nucleotides from each other. However, the probability that any particular pair of signal feature values occur together is much more effective in separating analytes. A two-dimensional plot of “Peak FFT whole 41” and “Cluster roughness” for dAMP and dCMP can be assigned with 77% accuracy using the pair-wise features (data not shown). While possible combinations of other features have been tested (data not shown), only the two just above mentioned gave the best separation between dAMP and dCMP. It is significantly larger than a random case of no separation with a value of P=0.5 (or 50%). Overall, Py can distinguish well between two analytes in any of six possible pairs consisted of four DNA nucleotides with accuracy of ˜82% on average (data not shown). In comparison, Iz can only distiguish between dAMP and dCMP with accuracy of 66% (data not shown), ˜10% lower than Py. In the best case, Py can achieve ˜89% separation with the two features alone for dCMP and dTMP, whereas Iz can only achieve 70% for the two nucleotides of dAMP and dGMP (data not shown).

As mentioned above, a 2-D plot was used to illustrate a more general and harder to visualize multi-dimensional (≥4D) analysis of feature ensembles to attain much higher accuracy. To do this, a Support Vector Machine (SVM), a machine-learning algorithm, was used to analyze the tunneling data.²² In brief, the SVM was first trained using a combination of randomly selected 10% subsets of the four data pools generated from the feature extraction for each nucleotide. The process iteratively reduced the 264 available signal features (see Example 5.4.3) to a range of smaller numbers that maintains a 100% separation for the training data (see FIG. 13A and FIG. 13B for training accuracy curves). As a result, the best 94 features were generated for Py and 55 for Iz (listed in Tables 8 and 9) to identify DNA nucleotides in the remaining 90% of the data.

TABLE 8 Independent features of Py for SVM to assign DNA nucleotides 1. Peak Max Amplitude 2. Peak Average Amplitude 3. Peak Top Average 4. Peak Width 5. Peak Total Power 6. Peak FFT 3 7. Peak FFT 6 8. Peak FFT 7 9. Peak FFT 8 10. Peak High Low Ratio 11. Peak FFT Whole 4 12. Peak FFT Whole 5 13. Peak FFT Whole 6 14. Peak FFT Whole 7 15. Peak FFT Whole 8 16. Peak FFT Whole 9 17. Peak FFT Whole 48 18. Peak FFT Whole 49 19. Cluster Average Amplitude 20. Cluster Roughness 21. Cluster Total Power 22. Cluster FFT Low 23. Cluster FFT High 24. Cluster FFT 1 25. Cluster FFT 2 26. Cluster FFT 3 27. Cluster FFT 11 28. Cluster FFT 12 29. Cluster FFT 13 30. Cluster FFT 14 31. Cluster FFT 15 32. Cluster FFT 16 33. Cluster FFT 17 34. Cluster FFT 18 35. Cluster FFT 23 36. Cluster FFT 25 37. Cluster FFT 26 38. Cluster FFT 27 39. Cluster FFT 28 40. Cluster FFT 29 41. Cluster FFT 30 42. Cluster FFT 31 43. Cluster FFT 32 44. Cluster FFT 38 45. Cluster FFT 39 46. Cluster FFT 40 47. Cluster FFT 41 48. Cluster FFT 42 49. Cluster FFT 43 50. Cluster FFT 44 51. Cluster FFT 47 52. Cluster FFT 48 53. Cluster FFT 49 54. Cluster FFT 50 55. Cluster FFT 51 56. Cluster FFT 52 57. Cluster FFT 53 58. Cluster FFT 54 59. Cluster FFT 55 60. Cluster FFT 56 61. Cluster FFT 57 62. Cluster FFT 58 63. Cluster FFT 59 64. Cluster FFT 60 65. Cluster FFT 61 66. Cluster High Low 67. Cluster Freq. Maximum Peaks 1 68. Cluster Freq. Maximum Peaks 2 69. Cluster FFT Whole 1 70. Cluster FFT Whole 16 71. Cluster FFT Whole 17 72. Cluster FFT Whole 18 73. Cluster FFT Whole 19 74. Cluster FFT Whole 20 75. Cluster FFT Whole 21 76. Cluster FFT Whole 22 77. Cluster FFT Whole 23 78. Cluster FFT Whole 24 79. Cluster FFT Whole 30 80. Cluster FFT Whole 31 81. Cluster FFT Whole 32 82. Cluster FFT Whole 33 83. Cluster FFT Whole 34 84. Cluster FFT Whole 35 85. Cluster FFT Whole 36 86. Cluster FFT Whole 37 87. Cluster FFT Whole 38 88. Cluster FFT Whole 39 89. Cluster FFT Whole 40 90. Cluster FFT Whole 41 91. Cluster FFT Whole 42 92. Cluster FFT Whole 47 93. Cluster FFT Whole 48 94. Cluster FFT Whole 49

TABLE 9 Independent features of Iz for SVM to assign DNA nucleotides 1. Peak Odd Even Ratio 2. Cluster Average Amplitude 3. Cluster Top Average 4. Cluster Cepstrum 3 5. Cluster Cepstrum 4 6. Cluster Cepstrum 5 7. Cluster Cepstrum 6 8. Cluster Cepstrum 7 9. Cluster Cepstrum 8 10. Cluster Cepstrum 9 11. Cluster Cepstrum 10 12. Cluster Cepstrum 11 13. Cluster Cepstrum 12 14. Cluster Cepstrum 13 15. Cluster Cepstrum 14 16. Cluster Cepstrum 15 17. Cluster Cepstrum 16 18. Cluster Cepstrum 17 19. Cluster Cepstrum 18 20. Cluster Cepstrum 19 21. Cluster Cepstrum 20 22. Cluster Cepstrum 21 23. Cluster Cepstrum 22 24. Cluster Cepstrum 23 25. Cluster Cepstrum 24 26. Cluster Cepstrum 25 27. Cluster Cepstrum 26 28. Cluster Cepstrum 27 29. Cluster Cepstrum 28 30. Cluster Cepstrum 29 31. Cluster Cepstrum 30 32. Cluster Cepstrum 31 33. Cluster Cepstrum 32 34. Cluster Cepstrum 33 35. Cluster Cepstrum 34 36. Cluster Cepstrum 35 37. Cluster Cepstrum 36 38. Cluster Cepstrum 37 39. Cluster Cepstrum 38 40. Cluster Cepstrum 39 41. Cluster Cepstrum 40 42. Cluster Cepstrum 41 43. Cluster Cepstrum 42 44. Cluster Cepstrum 43 45. Cluster Cepstrum 44 46. Cluster Cepstrum 45 47. Cluster Cepstrum 46 48. Cluster Cepstrum 47 49. Cluster Cepstrum 49 50. Cluster Cepstrum 52 51. Cluster Cepstrum 53 52. Cluster Cepstrum 54 53. Cluster Cepstrum 55 54. Cluster Cepstrum 56 55. Cluster Cepstrum 58

The SVM first removed those signals common to all the data obtained from different samples owing to contamination, capture events that were insensitive to chemical variation and noise spikes generated by the STM electronics and servo control, which amounted to about 30% of signal spikes, and then assigned the remaining signals to individual DNA nucleotides with the trained features (see Example 5.4.4). Such a method of training on a subset of all four data sets (collected with four microscopically-different tunnel junctions) sets an upper limit on accuracy (called the “optimistic” accuracy) of identifying an analyte. Practically, it can be achieved by calibrating the RT device with a standard solution prior to testing the real sample. As shown in FIG. 13A and FIG. 13B, the final accuracies fluctuate, depending on different combinations of those features. Table 10 lists the highest optimistic accuracy achieved by the SVM for identifying each individual DNA nucleotide in the plot.

TABLE 10 The accuracy (%) of both Py and Iz achieved in determining individual DNA nucleotides by RT dAMP dCMP dGMP dTMP Mean ± σ Py Optimistic 98.8 99.4 97.1 96.7 98.0 ± 1.3 Predictive 76.3 89.0 93.4 83.6 85.6 ± 7.4 Iz Optimistic 96.5 97.4 96.4 98.1 97.1 ± 0.8 Predictive 94.6 75.3 40.3 44.1 63.6 ± 26.0

With the Py reader, the four DNA nucleotides can be identified with accuracies ranging from 99.4% to 96.7, on average 98.0%, whereas Iz can read the DNA nucleotides with accuracy from 98.1% to 96.4, on average 97.1%. There is a ˜1% improvement to the optimistic accuracy from Iz to Py. Importantly, even a 0.1% improvement to accuracy is of great significance for sequencing of large genomes such as the human genome especially when the focus is on cancer mutations. This is because misidentification of 1 out of 1,000 nucleotides would result in about 3,000,000 false calls over a human genome that is composed of ˜3 billion of base pairs. It is challenging to confirm a single nucleotide variant (SNV) generated by NGS either owing to a true biological variant or a technical error.^(3,37) The RT technology directly reads DNA bases from their intrinsic properties, providing a method to reduce the uncertainties in detection of SNVs.

More interesting is the “predictive” accuracy, which was generated by training the SVM on data from three tunnel junctions and then using it to analyze data from the fourth junction. Py can read the four DNA nucleotides with its predictive accuracies ranging from 76% to 93%, on average of ˜86%, whereas Iz from 40% to 95%, on average of ˜64% with a much larger standard deviation (Table 10). These data manifest Py as a better reader molecule than Iz. Although the predictive accuracy of Py is modest, it can be improved by measuring the same sample with multiple times since the RT process is a stochastic. With the predictive capability, RT can be used to identify unknown samples that contain analytes that are already built in a RT database.

In conclusion, π-π stacking interaction can be used to identify DNA bases in the tunnel gap. Compared to Iz, the pyrene reader is not only more specific, but also more accurate to reading those canonical DNA bases. Interestingly, Iz recognizes the abasic site, which may be used to identify the depurination and depyrimidination in genomes when a comparison is run with data generated by Py. Preliminary analysis shows that the RT data of AP can be separated from those of DNA nucleotides generated by Py with optimistic accuracy of ˜95% (see Example 5.5, FIG. 14, and Table 7), but not from those generated by Iz. Thus, the RT sequencing with both Py and

Iz should collect more comprehensive information on genomic sequences, for example damages of DNA bases, which is lost in NGS due to the use of polymerases that can incorporate dAMP into the opposite of an abasic site (“A rule”³⁸) or cause a frameshift.³⁹

REFERENCES

The following references are hereby incorporated by reference in their entireties:

-   1. Goodwin S, McPherson J D, McCombie W R. Coming of age: ten years     of next-generation sequencing technologies. Nat Rev Genet 17,     333-351 (2016). -   2. Liu L, et al. Comparison of next-generation sequencing systems. J     Biomed Biotechnol 2012, 251364 (2012). -   3. Qi Y, et al. Reproducibility of Variant Calls in Replicate Next     Generation Sequencing Experiments. PLoS One 10, e0119230 (2015). -   4. Huang H, Keohavong P. Fidelity and predominant mutations produced     by deep vent wild-type and exonuclease-deficient DNA polymerases     during in vitro DNA amplification. DNA Cell Biol 15, 589-594 (1996). -   5. Paez J G, et al. Genome coverage and sequence fidelity of phi29     polymerase-based multiple strand displacement whole genome     amplification. Nucleic Acids Res 32, e71 (2004). -   6. Martincorena Ii, Campbell P J. Somatic mutation in cancer and     normal cells. Science 349, 1483-1489 (2015). -   7. Treangen T J, Salzberg S L. Repetitive DNA and next-generation     sequencing: computational challenges and solutions. Nat Rev Genet     13, 36-46 (2012). -   8. Mukhopadhyay R. DNA sequencers: the next generation. Anal Chem     81, 1736-1740 (2009). -   9. Laver T, et al. Assessing the performance of the Oxford Nanopore     Technologies MinION. Biomolecular Detection and Quantification 3,     1-8 (2015). -   10. Szalay T, Golovchenko J A. De novo sequencing and variant     calling with nanopores using PoreSeq. Nat Biotechnol 33, 1087-1091     (2015). -   11. Jain M, Fiddes I T, Miga K H, Olsen H E, Paten B, Akeson M.     Improved data analysis for the MinION nanopore sequencer. Nat     Methods 12, 351-356 (2015). -   12. Laszlo A H, et al. Decoding long nanopore sequencing reads of     natural DNA. Nat Biotechnol 32, 829-833 (2014). -   13. Manrao E A, et al. Reading DNA at single-nucleotide resolution     with a mutant MspA nanopore and phi29 DNA polymerase. Nat Biotechnol     30, 349-353 (2012). -   14. Lindsay S. The promises and challenges of solid-state     sequencing. Nat Nanotechnol 11, 109-111 (2016). -   15. Zwolak M, Ventra M D. Electronic Signature of DNA Nucleotides     via Transverse

Transport. Nano Lett 5, 421-424 (2005).

-   16. Tsutsui M, Taniguchi M, Yokota K, Kawai T. Identifying single     nucleotides by tunnelling current. Nat Nanotechnol 5, 286-290     (2010). -   17. Ohshiro T, Matsubara K, Tsutsui M, Furuhashi M, Taniguchi M,     Kawai T. Single-molecule electrical random resequencing of DNA and     RNA. Sci Rep 2, 501 (2012). -   18. Erdmann M, David R, Fornof A R, Gaub H E. Electrically induced     bonding of DNA to gold. Nat Chem 2, 745-749 (2010). -   19. Bano F, Sluysmans D, Wislez A, Duwez A S. Unraveling the     complexity of the interactions of DNA nucleotides with gold by     single molecule force spectroscopy. Nanoscale 7, 19528-19533 (2015). -   20. Kimura-Suda H, Petrovykh D Y, Tarlov M J, Whitman L J.     Base-Dependent Competitive Adsorption of Single-Stranded DNA on     Gold. J Am Chem Soc 125, 9014-9015 (2003). -   21. Liang F, Li S, Lindsay S, Zhang P. Synthesis, Physicochemical     Properties, and Hydrogen Bonding of     4(5)-Substituted-1H-imidazole-2-carboxamide, A Potential Universal     Reader for DNA Sequencing by Recognition Tunneling. Chemistry—a     European Journal 18, 5998 6007 (2012). -   22. Chang S, et al. Chemical recognition and binding kinetics in a     functionalized tunnel junction. Nanotechnology 23, 235101 (2012). -   23. Zhao Y, et al. Single-molecule spectroscopy of amino acids and     peptides by recognition tunnelling. Nature Nanotechnology 9, 466-473     (2014). -   24. Im J, et al. Electronic Single Molecule Identification of     Carbohydrate Isomers by Recognition Tunneling. arXiv, 1601.04221     (2016). -   25. Petersheimf M, Turner D H. Base-Stacking and Base-Pairing     Contributions to Helix Stability: Thermodynamics of Double-Helix     Formation with CCGG, CCGGp, CCGGAp, ACCGGp, CCGGUp, and ACCGGUp.     Biochemistry 22, 256-263 (1983). -   26. Yakovchuk P, Protozanova E, Frank-Kamenetskii M D. Base-stacking     and base-pairing contributions into thermal stability of the DNA     double helix. Nucleic Acids Res 34, 564-574 (2006). -   27. Riley K E, Hobza P H. On the Importance and Origin of Aromatic     Interactions in Chemistry and Biodisciplines. ACCOUNTS OF CHEMICAL     RESEARCH 46, 927-936 (2013). -   28. Kelley S O, Barton J K. Electron Transfer Between Bases in     Double Helical DNA. Science 283, 375-381 (1999). -   29. Xu B, Zhang P, Li X, Tao N. Direct Conductance Measurement of     Single DNA Molecules in Aqueous Solution. Nano letters 4, 1105-1108     (2004). -   30. Guckian K M, Schweitzer B A, Ren R X-F, Sheils C J, Tahmassebi D     C, Kool E T. Factors Contributing to Aromatic Stacking in Water:     Evaluation in the Context of DNA. J Am Chem Soc 122, 2213-2222     (2000). -   31. Swart M, van der Wij st T, Fonseca Guerra C, Bickelhaupt F M.     Pi-pi stacking tackled with density functional theory. J Mol Model     13, 1245-1257 (2007). -   32. Lai J S, Qu J, Kool E T. Fluorinated DNA bases as probes of     electrostatic effects in DNA base stacking. Angew Chem Int Ed Engl     42, 5973-5977 (2003). -   33. Liang F, Li S, Lindsay S, Zhang P. Synthesis, physicochemical     properties, and hydrogen bonding of 4(5)-substituted     1-H-imidazole-2-carboxamide, a potential universal reader for DNA     sequencing by recognition tunneling. Chemistry (Easton) 18,     5998-6007 (2012). -   34. Chang S, et al. Palladium electrodes for molecular tunnel     junctions. Nanotechnology 23, 425202 (2012). -   35. Chang S, He J, Zhang P, Gyarfas B, Lindsay S. Gap distance and     interactions in a molecular tunnel junction. J Am Chem Soc 133,     14267-14269 (2011). -   36. Grimme S. Do special noncovalent pi-pi stacking interactions     really exist? Angew Chem Int Ed Engl 47, 3430-3434 (2008). -   37. Robasky K, Lewis N E, Church G M. The role of replicates for     error mitigation in next-generation sequencing. Nat Rev Genet 15,     56-62 (2014). -   38. Strauss B S. The “A” rule revisited: polymerases as determinants     of mutational specificity. DNA Repair 1, 125-135 (2002). -   39. Patra A, Zhang Q, Lei L, Su Y, Egli M, Guengerich F P.     Structural and kinetic analysis of nucleoside triphosphate     incorporation opposite an abasic site by human translesion DNA     polymerase eta. J Biol Chem 290, 8028-8038 (2015).

While particular materials, formulations, operational sequences, process parameters, and end products have been set forth to describe and exemplify this invention, they are not intended to be limiting. Rather, it should be noted by those ordinarily skilled in the art that the written disclosures are exemplary only and that various other alternatives, adaptations, and modifications may be made within the scope of the present invention. Accordingly, the present invention is not limited to the specific embodiments illustrated herein, but is limited only by the following claims. 

What is claimed is:
 1. A compound of formula (I):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; R₂ is selected from the group consisting of thiol, disulfide, and amine; and R₃ is H, an electron withdrawing group or an electron donating group; ring A or ring B is substituted with R₁-R₂; and ring C or ring D is substituted with R₃; provided that (i) when ring A is substituted with R₁-R₂, ring C is substituted with R₃; and (ii) when ring B is substituted with R₁-R₂, ring D is substituted with R₃.
 2. The compound of claim 1, wherein R₁ is (C₁-C₆) alkyl.
 3. The compound of claim 1, wherein R₁ is (C₁-C₃) alkyl.
 4. The compound of claim 1, wherein R₁ is ethyl.
 5. The compound of claim 1, wherein R₂ is thiol.
 6. The compound of claim 2, wherein R₂ is thiol.
 7. The compound of claim 1, wherein R₃ is H, —NO₂ or —CH₃.
 8. The compound of claim 6, wherein R₃ is H, —NO₂ or —CH₃.
 9. The compound of claim 1, wherein R₃ is H.
 10. The compound of claim 8, wherein R₃ is H
 11. The compound of claim 1, wherein said compound is formula (Ia):

wherein R₁ is (C₁-C₆) alkyl or (C₁-C₆) alkoxy; and R₂ is selected from the group consisting of thiol, disulfide, and amine.
 12. The compound of claim 11, wherein R₁ is (C₁-C₆) alkyl.
 13. The compound of claim 11, wherein R₁ is (C₁-C₃) alkyl.
 14. The compound of claim 11, wherein R₁ is ethyl.
 15. The compound of claim 11, wherein R₂ is thiol.
 16. The compound of claim 12, wherein R₂ is thiol.
 17. The compound of claim 13, wherein R₂ is thiol.
 18. The compound of claim 14, wherein R₂ is thiol.
 19. A compound of formula (I):

wherein R₁ is (C₁-C₃) alkyl; R₂ is thiol; and R₃ is H; ring A or ring B is substituted with R₁-R₂; and ring C or ring D is substituted with R₃; provided that (i) when ring A is substituted with R₁-R₂, ring C is substituted with R₃; and (ii) when ring B is substituted with R₁-R₂, ring D is substituted with R₃.
 20. The compound of claim 19, wherein said compound is formula (Ia): 